Data-SMARTS Requirement

Data Cleaning

The system must provide comprehensive data validation, quality assurance, and cleaning capabilities to ensure data integrity throughout the collection and processing lifecycle.

Required Capabilities

Format Validation

Ensure data matches expected formats

Range Checks

Verify values within acceptable ranges

Duplicate Detection

Identify and resolve duplicate records

Audit Trails

Track all changes with full history

Detailed Requirements

DQ-1

Real-Time Validation

Field-level validation during data entry (format, range, required fields)
Cross-field consistency checks (e.g., age vs grade level)
Immediate feedback to data collectors on validation errors
Configurable validation rules per assessment type

DQ-2

Duplicate Detection

Automatic detection of duplicate records based on configurable criteria
Fuzzy matching for name variations and data entry errors
Duplicate resolution workflow with merge capabilities
Prevention of duplicate submissions through unique identifiers

DQ-3

Anomaly Detection

Statistical outlier detection for assessment scores
Pattern analysis to identify suspicious data (e.g., identical scores)
Flagging of unusual response patterns for review
Configurable thresholds for anomaly alerts

DQ-4

Data Completeness

Monitoring of required field completion rates
Identification of partially completed assessments
Tracking of missing data by school, region, and monitor
Alerts for incomplete data submissions

DQ-5

Review Workflows

Multi-level review and approval process for flagged data
Assignment of review tasks to supervisors
Ability to correct, approve, or reject questionable data
Escalation paths for unresolved data quality issues

DQ-6

Audit & Traceability

Complete audit trail of all data modifications
Recording of original values before any changes
User attribution for all edits and approvals
Timestamp tracking for data lifecycle events

Validation Pipeline


┌─────────────────────────────────────────────────────────────────────────────────┐
│                         DATA QUALITY PIPELINE                                    │
└─────────────────────────────────────────────────────────────────────────────────┘

  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
  │   Data      │     │  Real-Time  │     │   Server    │     │   Review    │
  │   Entry     │────▶│ Validation  │────▶│ Validation  │────▶│  Workflow   │
  └─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
        │                   │                   │                   │
        │                   ▼                   ▼                   ▼
        │            ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
        │            │ • Format    │     │ • Duplicate │     │ • Flagged   │
        │            │ • Required  │     │ • Anomaly   │     │   Records   │
        │            │ • Range     │     │ • Cross-ref │     │ • Approval  │
        │            └─────────────┘     └─────────────┘     │ • Rejection │
        │                                                    └─────────────┘
        │
        │         ┌──────────────────────────────────────────────────────┐
        └────────▶│                   AUDIT LOG                          │
                  │  • Original values  • User  • Timestamp  • Action   │
                  └──────────────────────────────────────────────────────┘

See Our Implementation

The Universal Learning Portal backend implements these data quality requirements with comprehensive validation and audit capabilities.

View Backend System